#!/usr/bin/env python
#-*- coding:utf-8 -*-
# Author: LiuHuan
# Datetime: 2020/3/26 12:32

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import csv
import os
import modeling
import optimization
import tokenization
import tensorflow as tf
import numpy as np
import spacy
import json
nlp = spacy.load("en_core_sci_sm")


LABELS = ["0","1","2","3","4"]

with open('config.txt','r',encoding='utf=8') as f:
    config_str = ''.join([line.strip() for line in f.readlines()])
config = json.loads(config_str)


MODEL_PATH = config['MODEL_PATH']
BERT_PATH = config['BERT_PATH']
MASK_SEQ_LENGTH = 512


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
          guid: Unique id for the example.
          text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
          text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
          label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_test_examples(self, data_dir):
        """Gets a collection of `InputExample`s for prediction."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with tf.gfile.Open(input_file, "r") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines


class MoveProcessor(DataProcessor):
    """Processor for the move data set ."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return LABELS

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            # Only the test set has a header
            # if set_type == "test" and i == 0:
            #   continue
            guid = "%s-%s" % (set_type, i)
            if set_type == "test":
                text_a = tokenization.convert_to_unicode(line[0])
                label = "0"
            else:
                text_a = tokenization.convert_to_unicode(line[1])
                label = tokenization.convert_to_unicode(line[0])
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples


def convert_single_example(ex_index, example, label_list, max_seq_length,
                           tokenizer):
    """Converts a single `InputExample` into a single `InputFeatures`."""
    label_map = {}
    for (i, label) in enumerate(label_list):
        label_map[label] = i

    tokens_a = tokenizer.tokenize(example.text_a)
    tokens_b = None
    if example.text_b:
        tokens_b = tokenizer.tokenize(example.text_b)

    if tokens_b:
        # Modifies `tokens_a` and `tokens_b` in place so that the total
        # length is less than the specified length.
        # Account for [CLS], [SEP], [SEP] with "- 3"
        _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
    else:
        # Account for [CLS] and [SEP] with "- 2"
        if len(tokens_a) > max_seq_length - 2:
            tokens_a = tokens_a[0:(max_seq_length - 2)]

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids: 0     0   0   0  0     0 0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens = []
    segment_ids = []
    tokens.append("[CLS]")
    segment_ids.append(0)
    for token in tokens_a:
        tokens.append(token)
        segment_ids.append(0)
    tokens.append("[SEP]")
    segment_ids.append(0)

    if tokens_b:
        for token in tokens_b:
            tokens.append(token)
            segment_ids.append(1)
        tokens.append("[SEP]")
        segment_ids.append(1)

    input_ids = tokenizer.convert_tokens_to_ids(tokens)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real
    # tokens are attended to.
    input_mask = [1] * len(input_ids)

    # Zero-pad up to the sequence length.
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length

    label_id = label_map[example.label]
    # if ex_index < 5:
    #     tf.logging.info("*** Example ***")
    #     tf.logging.info("guid: %s" % (example.guid))
    #     tf.logging.info("tokens: %s" % " ".join(
    #         [tokenization.printable_text(x) for x in tokens]))
    #     tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
    #     tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
    #     tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
    #     tf.logging.info("label: %s (id = %d)" % (example.label, label_id))

    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_id=label_id)
    return feature


def file_based_convert_examples_to_features(
        examples, label_list, max_seq_length, tokenizer, output_file):
    """Convert a set of `InputExample`s to a TFRecord file."""

    writer = tf.python_io.TFRecordWriter(output_file)

    for (ex_index, example) in enumerate(examples):
        # if ex_index % 10000 == 0:
        #     tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

        feature = convert_single_example(ex_index, example, label_list,
                                         max_seq_length, tokenizer)

        def create_int_feature(values):
            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
            return f

        features = collections.OrderedDict()
        features["input_ids"] = create_int_feature(feature.input_ids)
        features["input_mask"] = create_int_feature(feature.input_mask)
        features["segment_ids"] = create_int_feature(feature.segment_ids)
        features["label_ids"] = create_int_feature([feature.label_id])

        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(tf_example.SerializeToString())


def file_based_input_fn_builder(input_file, seq_length, is_training,
                                drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "label_ids": tf.FixedLenFeature([], tf.int64),
    }

    def _decode_record(record, name_to_features):
        """Decodes a record to a TensorFlow example."""
        example = tf.parse_single_example(record, name_to_features)

        # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
        # So cast all int64 to int32.
        for name in list(example.keys()):
            t = example[name]
            if t.dtype == tf.int64:
                t = tf.to_int32(t)
            example[name] = t

        return example

    def input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]

        # For training, we want a lot of parallel reading and shuffling.
        # For eval, we want no shuffling and parallel reading doesn't matter.
        d = tf.data.TFRecordDataset(input_file)
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)

        d = d.apply(
            tf.contrib.data.map_and_batch(
                lambda record: _decode_record(record, name_to_features),
                batch_size=batch_size,
                drop_remainder=drop_remainder))

        return d

    return input_fn


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                 labels, num_labels, use_one_hot_embeddings):
    """Creates a classification model."""
    model = modeling.BertModel(
        config=bert_config,
        is_training=is_training,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=segment_ids,
        use_one_hot_embeddings=use_one_hot_embeddings)

    # In the demo, we are doing a simple classification task on the entire
    # segment.
    #
    # If you want to use the token-level output, use model.get_sequence_output()
    # instead.
    output_layer = model.get_pooled_output()

    hidden_size = output_layer.shape[-1].value

    output_weights = tf.get_variable(
        "output_weights", [num_labels, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))

    output_bias = tf.get_variable(
        "output_bias", [num_labels], initializer=tf.zeros_initializer())

    with tf.variable_scope("loss"):
        if is_training:
            # I.e., 0.1 dropout
            output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

        logits = tf.matmul(output_layer, output_weights, transpose_b=True)
        logits = tf.nn.bias_add(logits, output_bias)
        probabilities = tf.nn.softmax(logits, axis=-1)
        log_probs = tf.nn.log_softmax(logits, axis=-1)

        one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)

        per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
        loss = tf.reduce_mean(per_example_loss)

        return (loss, per_example_loss, logits, probabilities)


def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
    """Returns `model_fn` closure for TPUEstimator."""

    def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
        """The `model_fn` for TPUEstimator."""

        # tf.logging.info("*** Features ***")
        # for name in sorted(features.keys()):
        #     tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

        input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]
        label_ids = features["label_ids"]

        is_training = (mode == tf.estimator.ModeKeys.TRAIN)

        (total_loss, per_example_loss, logits, probabilities) = create_model(
            bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
            num_labels, use_one_hot_embeddings)

        tvars = tf.trainable_variables()

        scaffold_fn = None
        initialized_variable_names = []
        if init_checkpoint:
            (assignment_map, initialized_variable_names
             ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
            if use_tpu:

                def tpu_scaffold():
                    tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
                    return tf.train.Scaffold()

                scaffold_fn = tpu_scaffold
            else:
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

        # tf.logging.info("**** Trainable Variables ****")
        # for var in tvars:
        #     init_string = ""
        #     if var.name in initialized_variable_names:
        #         init_string = ", *INIT_FROM_CKPT*"
        #     tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
        #                     init_string)

        output_spec = None
        if mode == tf.estimator.ModeKeys.TRAIN:

            train_op = optimization.create_optimizer(
                total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)

            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                train_op=train_op,
                scaffold_fn=scaffold_fn)
        elif mode == tf.estimator.ModeKeys.EVAL:

            def metric_fn(per_example_loss, label_ids, logits):
                predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
                accuracy = tf.metrics.accuracy(label_ids, predictions)
                loss = tf.metrics.mean(per_example_loss)
                return {
                    "eval_accuracy": accuracy,
                    "eval_loss": loss,
                }

            eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                eval_metrics=eval_metrics,
                scaffold_fn=scaffold_fn)
        else:
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode, predictions=probabilities, scaffold_fn=scaffold_fn)
        return output_spec

    return model_fn


# This function is not used by this file but is still used by the Colab and
# people who depend on it.
def input_fn_builder(features, seq_length, is_training, drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    all_input_ids = []
    all_input_mask = []
    all_segment_ids = []
    all_label_ids = []

    for feature in features:
        all_input_ids.append(feature.input_ids)
        all_input_mask.append(feature.input_mask)
        all_segment_ids.append(feature.segment_ids)
        all_label_ids.append(feature.label_id)

    def input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]

        num_examples = len(features)

        # This is for demo purposes and does NOT scale to large data sets. We do
        # not use Dataset.from_generator() because that uses tf.py_func which is
        # not TPU compatible. The right way to load data is with TFRecordReader.
        d = tf.data.Dataset.from_tensor_slices({
            "input_ids":
                tf.constant(
                    all_input_ids, shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "input_mask":
                tf.constant(
                    all_input_mask,
                    shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "segment_ids":
                tf.constant(
                    all_segment_ids,
                    shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "label_ids":
                tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
        })

        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)

        d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
        return d

    return input_fn


# This function is not used by this file but is still used by the Colab and
# people who depend on it.
def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer):
    """Convert a set of `InputExample`s to a list of `InputFeatures`."""

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

        feature = convert_single_example(ex_index, example, label_list,
                                         max_seq_length, tokenizer)

        features.append(feature)
    return features

def predict_abstracts():
    with open(config['INPUT_FILE'],'r',encoding='utf-8') as f:
        texts = [line.strip() for line in f.readline()]

    tf.logging.set_verbosity(tf.logging.INFO)

    processors = {
        "bert_move": MoveProcessor
    }

    bert_config = modeling.BertConfig.from_json_file(BERT_PATH + r"\bert_config.json")


    processor = processors['bert_move']()

    label_list = processor.get_labels()

    tokenizer = tokenization.FullTokenizer(
        vocab_file=BERT_PATH + r"\vocab.txt", do_lower_case=True)

    tpu_cluster_resolver = None

    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
    run_config = tf.contrib.tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=None,
        model_dir=MODEL_PATH,
        save_checkpoints_steps=1000,
        tpu_config=tf.contrib.tpu.TPUConfig(
            iterations_per_loop=1000,
            num_shards=8,
            per_host_input_for_training=is_per_host))

    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    # if False:
    #     train_examples = processor.get_train_examples(FLAGS.data_dir)
    #     num_train_steps = int(
    #         len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    #     num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

    model_fn = model_fn_builder(
        bert_config=bert_config,
        num_labels=len(label_list),
        init_checkpoint= BERT_PATH + r"\bert_model.ckpt",
        learning_rate=2e-5,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=False,
        use_one_hot_embeddings=False)

    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=False,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=32,
        eval_batch_size=8,
        predict_batch_size=8)

    predict_examples_1 = []
    predict_examples_2 = []


    nums = [] # 保存每篇摘要句子数量

    sentences = []
    for text in texts:
        sens = [str(sen)for sen in nlp(text).sents]
        sentences += sens
        nums.append(len(sens))
        i = 0
        for sen in sentences:
            masked_abs = ''
            mask = ' AAAA ' + 'AAAA ' * 23 + 'AAAA. '
            if i == 0:
                masked_abs = mask + ' '.join(sentences[1:])
            elif i == len(sentences) - 1:
                masked_abs = ' '.join(sentences[:-1]) + mask
            else:
                masked_abs = ' '.join(sentences[:i]) + mask + ' '.join(sentences[i + 1:])
            # print(masked_abs)
            guid = 'test-' + str(i)
            predict_examples_2.append(InputExample(guid=guid, text_a=masked_abs, text_b=None, label='0'))
            i += 1
        for sen in sentences:
            guid = 'test-' + str(i)
            predict_examples_1.append(InputExample(guid=guid, text_a=sen, text_b=None, label='0'))
            i += 1

    predict_file = os.path.join(MODEL_PATH, "predict.tf_record")
    file_based_convert_examples_to_features(predict_examples_1, label_list,
                                            MASK_SEQ_LENGTH, tokenizer,
                                            predict_file)

    predict_drop_remainder = False
    predict_input_fn = file_based_input_fn_builder(
        input_file=predict_file,
        seq_length=MASK_SEQ_LENGTH,
        is_training=False,
        drop_remainder=predict_drop_remainder)

    result_1 = estimator.predict(input_fn=predict_input_fn)

    predict_file = os.path.join(MODEL_PATH, "predict.tf_record")
    file_based_convert_examples_to_features(predict_examples_2, label_list,
                                            MASK_SEQ_LENGTH, tokenizer,
                                            predict_file)

    predict_drop_remainder = False
    predict_input_fn = file_based_input_fn_builder(
        input_file=predict_file,
        seq_length=MASK_SEQ_LENGTH,
        is_training=False,
        drop_remainder=predict_drop_remainder)

    result_2 = estimator.predict(input_fn=predict_input_fn)




    result1 = [r.tolist() for r in result_1]
    result2 = [r.tolist() for r in result_2]

    results = np.array(result1) + np.array(result2)
    results = results.tolist()

    cla = {'0': 'Purpose', '1': 'Methods', '2': 'Results', '3': 'Conclusions','4':'Background'}
    i = 0
    with open(config['OUTPUT_FILE'],'w',encoding='utf-8') as f:
        for num in nums:
            result_temp = results[i:i+num]
            for a in result_temp:
                f.write(cla[str(a.index(max(a)))] + ': ' + sentences[i] + '\n')
            f.write('\n')
            i += num

    return results,sentences


def predict_single_abs():
    with open(config['INPUT_FILE'],'r',encoding='utf-8') as f:
        text = f.readline().strip()

    tf.logging.set_verbosity(tf.logging.INFO)

    processors = {
        "bert_move": MoveProcessor
    }

    bert_config = modeling.BertConfig.from_json_file(BERT_PATH + r"\bert_config.json")


    processor = processors['bert_move']()

    label_list = processor.get_labels()

    tokenizer = tokenization.FullTokenizer(
        vocab_file=BERT_PATH + r"\vocab.txt", do_lower_case=True)

    tpu_cluster_resolver = None

    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
    run_config = tf.contrib.tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=None,
        model_dir=MODEL_PATH,
        save_checkpoints_steps=1000,
        tpu_config=tf.contrib.tpu.TPUConfig(
            iterations_per_loop=1000,
            num_shards=8,
            per_host_input_for_training=is_per_host))

    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    # if False:
    #     train_examples = processor.get_train_examples(FLAGS.data_dir)
    #     num_train_steps = int(
    #         len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    #     num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

    model_fn = model_fn_builder(
        bert_config=bert_config,
        num_labels=len(label_list),
        init_checkpoint= BERT_PATH + r"\bert_model.ckpt",
        learning_rate=2e-5,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=False,
        use_one_hot_embeddings=False)

    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=False,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=32,
        eval_batch_size=8,
        predict_batch_size=8)

    predict_examples = []
    # text = input()
    sentences = [str(sen) for sen in nlp(text).sents]

    i = 0
    for sen in sentences:
        masked_abs = ''
        mask = ' AAAA ' + 'AAAA ' * 23 + 'AAAA. '
        if i == 0:
            masked_abs = mask + ' '.join(sentences[1:])
        elif i == len(sentences) - 1:
            masked_abs = ' '.join(sentences[:-1]) + mask
        else:
            masked_abs = ' '.join(sentences[:i]) + mask + ' '.join(sentences[i + 1:])
        print(masked_abs)
        guid = 'test-' + str(i)
        predict_examples.append(InputExample(guid=guid, text_a=masked_abs, text_b=None, label='0'))
        i += 1
    for sen in sentences:
        guid = 'test-' + str(i)
        predict_examples.append(InputExample(guid=guid, text_a=sen, text_b=None, label='0'))
        i += 1

    predict_file = os.path.join(MODEL_PATH, "predict.tf_record")
    file_based_convert_examples_to_features(predict_examples, label_list,
                                            MASK_SEQ_LENGTH, tokenizer,
                                            predict_file)

    # tf.logging.info("***** Running prediction*****")
    # tf.logging.info("  Num examples = %d", len(predict_examples))
    # tf.logging.info("  Batch size = %d", 8)

    # if FLAGS.use_tpu:
    #     # Warning: According to tpu_estimator.py Prediction on TPU is an
    #     # experimental feature and hence not supported here
    #     raise ValueError("Prediction in TPU not supported")

    predict_drop_remainder = False
    predict_input_fn = file_based_input_fn_builder(
        input_file=predict_file,
        seq_length=MASK_SEQ_LENGTH,
        is_training=False,
        drop_remainder=predict_drop_remainder)

    result = estimator.predict(input_fn=predict_input_fn)
    result1 = []
    result2 = []
    print(i)
    j = 1
    for r in result:
        if j <= i/2:
            result1.append(r.tolist())
        else:
            result2.append(r.tolist())
        j += 1
    print(j)
    print('result1: ',result1)
    print('result2: ',result2)
    results = np.array(result1) + np.array(result2)
    print('results: ',results)
    results = results.tolist()
    print('results: ',results)

    cla = {'0': 'Purpose', '1': 'Methods', '2': 'Results', '3': 'Conclusions','4':'Background'}
    i = 0
    with open(config['OUTPUT_FILE'],'w',encoding='utf-8') as f:
        for a in results:
            m = max(a)
            f.write(cla[str(a.index(max(a)))] + ': ' + sentences[i] + '\n')
            print('<<' + cla[str(a.index(max(a)))] + '>> ' + sentences[i])
            i += 1

    return results,sentences

if __name__ == "__main__":

    if config['SINGLE_ABSTRACT']:
        predict_single_abs()
